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+---
+layout: integration
+name: Hugging Face
+description: Use Models on Hugging Face with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/farm-haystack
+repo: https://github.com/deepset-ai/haystack
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/huggingface.png
+---
+
+You can use models on [Hugging Face](https://huggingface.co/) in your Haystack pipelines with the [PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node), [EmbeddingRetriever](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended), [Ranker](https://docs.haystack.deepset.ai/docs/ranker), [Reader](https://docs.haystack.deepset.ai/docs/reader) and more!
+
+## Installation
+
+```bash
+pip install farm-haystack
+```
+
+## Usage
+
+You can use models on Hugging Face in various ways:
+
+### Embedding Models
+
+To use embedding models on Hugging Face, initialize an `EmbeddingRetriever` with the model name. You can then use this `EmbeddingRetriever` in an indexing pipeline to create semantic embeddings for documents and index them to a document store. 
+
+Below is the example indexing pipeline with `PreProcessor`, `InMemoryDocumentStore` and  `EmbeddingRetriever`:
+
+```python
+from haystack.nodes import EmbeddingRetriever
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.pipelines import Pipeline
+from haystack.schema import Document
+
+document_store = InMemoryDocumentStore(embedding_dim=384)
+preprocessor = PreProcessor()
+retriever = EmbeddingRetriever(
+    embedding_model="sentence-transformers/all-MiniLM-L6-v2", document_store=document_store
+)
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_node(component=preprocessor, name="Preprocessor", inputs=["File"])
+indexing_pipeline.add_node(component=retriever, name="Retriever", inputs=["Preprocessor"])
+indexing_pipeline.add_node(component=document_store, name="document_store", inputs=["Retriever"])
+indexing_pipeline.run(documents=[Document("This is my document")])
+```
+
+### Generative Models (LLMs) 
+
+To use text generation models on Hugging Face, initialize a `PromptNode` with the model name and the prompt template. You can then use this `PromptNode` to generate questions from the given context.  
+
+Below is the example of question generation pipeline using RAG with `EmbeddingRetriever` and  `PromptNode`:
+
+```python
+from haystack import Pipeline
+from haystack.nodes import BM25Retriever, PromptNode
+
+retriever = EmbeddingRetriever(
+    embedding_model="sentence-transformers/all-MiniLM-L6-v2", document_store=document_store
+)
+prompt_node = PromptNode(model_name_or_path = "mistralai/Mistral-7B-Instruct-v0.1",
+                         api_key = "HF_API_KEY",
+                         default_prompt_template = "deepset/question-generation")
+query_pipeline = Pipeline()
+query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])
+
+query_pipeline.run(query = "Berlin")
+```
+
+> If you would like to use the [Inference API](https://huggingface.co/inference-api), you need pass your Hugging Face token to PromptNode.
+
+
+### Ranker Models
+
+To use cross encoder models on Hugging Face, initialize a `SentenceTransformersRanker` with the model name. You can then use this `SentenceTransformersRanker` to sort documents based on their relevancy to the query.
+
+Below is the example of document retrieval pipeline with `BM25Retriever` and  `SentenceTransformersRanker`:
+
+```python
+from haystack.nodes import SentenceTransformersRanker, BM25Retriever
+from haystack.pipelines import Pipeline
+
+retriever = BM25Retriever(document_store=document_store)
+ranker = SentenceTransformersRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2")
+
+document_retrieval_pipeline = Pipeline()
+document_retrieval_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+document_retrieval_pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])
+document_retrieval_pipeline.run("YOUR_QUERY")
+```
+
+### Reader Models
+
+To use question answering models on Hugging Face, initialize a `FarmReader` with the model name. You can then use this `FarmReader` to extract answers from the relevant context.
+
+Below is the example of extractive question answering pipeline with `BM25Retriever` and  `FARMReader`:
+
+```python
+from haystack.nodes import BM25Retriever, FARMReader
+from haystack.pipelines import Pipeline
+
+retriever = BM25Retriever(document_store=document_store)
+reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
+
+querying_pipeline = Pipeline()
+querying_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+querying_pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"])
+querying_pipeline.run("YOUR_QUERY")
+```
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